|
| float | test_Solvers.A1 = rng.random((n, n)) + 0.001 * np.eye(n) |
| |
| | test_Solvers.A_eq = rng.random((neq, n)) |
| |
| | test_Solvers.A_in = rng.random((nin, n)) |
| |
| float | test_Solvers.A_lb = rng.random(nin) * NORMAL_DISTR_VAR |
| |
| float | test_Solvers.A_ub = rng.random(nin) * NORMAL_DISTR_VAR |
| |
| | test_Solvers.b1 = rng.random(n) |
| |
| | test_Solvers.b_eq = A_eq.dot(x) |
| |
| | test_Solvers.const1 = tsid.ConstraintLevel() |
| |
| | test_Solvers.const2 = tsid.ConstraintLevel() |
| |
| | test_Solvers.constrVal = A_in.dot(x) |
| |
| | test_Solvers.cost = tsid.ConstraintEquality("c1", A1, b1) |
| |
| int | test_Solvers.damping = 1e-10 |
| |
| int | test_Solvers.EPS = 1e-3 |
| |
| | test_Solvers.eq_const = tsid.ConstraintEquality("eq1", A_eq, b_eq) |
| |
| int | test_Solvers.GRADIENT_PERTURBATION_VARIANCE = 1e-2 |
| |
| list | test_Solvers.gradientPerturbations = [] |
| |
| int | test_Solvers.HESSIAN_PERTURBATION_VARIANCE = 1e-1 |
| |
| list | test_Solvers.hessianPerturbations = [] |
| |
| | test_Solvers.HQPData = tsid.HQPData() |
| |
| | test_Solvers.HQPoutput = solver.solve(HQPData) |
| |
| | test_Solvers.in_const = tsid.ConstraintInequality("ini1", A_in, A_lb, A_ub) |
| |
| int | test_Solvers.MARGIN_PERC = 1e-3 |
| |
| int | test_Solvers.n = 60 |
| |
| int | test_Solvers.neq = 36 |
| |
| int | test_Solvers.nin = 40 |
| |
| float | test_Solvers.NORMAL_DISTR_VAR = 10.0 |
| |
| int | test_Solvers.nTest = 100 |
| |
| | test_Solvers.rng = np.random.default_rng(seed=0) |
| |
| | test_Solvers.solver_eiquadprog = tsid.SolverHQuadProg("eiquadprog solver") |
| |
| list | test_Solvers.solver_list = [] |
| |
| | test_Solvers.solver_osqp = tsid.SolverOSQP("osqp solver") |
| |
| | test_Solvers.solver_proxqp = tsid.SolverProxQP("proxqp solver") |
| |
| | test_Solvers.x = np.linalg.solve(A1, b1) |
| |